Psychosocial Factors of Post-operative Pain Intensity in Women Undergoing Cesarean Section
Bibliographic record
Abstract
Background: Little evidence has noted that psychological factors are risk factors of post-operative pain intensity in women undergoing cesarean section. Objectives: The aim of study was to determine predictive psychosocial factors for post-cesarean pain intensity using assessment of depression, anxiety, self-efficacy, and quality of relationship. Methods: This prospective descriptive-analytic study was carried out on 150 healthy women scheduled for cesarean section under spinal anesthesia. The day before the surgery, the patients completed three questionnaires including Hospital Anxiety and Depression Scale (HADS), and General Self-efficacy. Also, 24 hours after the surgery, the intensity of pain in the patients was assessed with filling McGill Pain Questionnaire (MPQ). Linear regression was used to predict the factors of pain intensity. Results: The anxiety was a positive predictor of pain intensity of women after C-section (β = 0. 0.22, P = 0.014). However, depression score, and self-efficacy were not predicting factors of pain intensity of women after C-section. Conclusions: Preoperative anxiety increases post-operative pain intensity in women undergoing cesarean section.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".